Dependencies

To build and run the files, you will need the following libraries:

• scikit-learn 0.21.2
• matplotlib 3.1.1
• pycpd 1.0.5
• numpy 1.16.2
• open3d-python 0.7.0.0
• scipy 1.3.0
• h5py 2.9.0

How to make it work

To run the whole project, you are required to follow some steps. The parameters of each function will be explained in the next subsection.

1. Download the Extreme 3D Faces and follow the instructions to make it work correctly. Otherwise, you will need some 3D models from another CNN in a .ply format (in this case, you will also need to adapt the next steps).
2. Save the .ply files of the network in the filePlyRete folder.
3. Get the FaceWarehouse dataset
4. Copy the data of the dataset in a faceWarehouse folder.
5. Run the plyTotxt() function in plyConverter.py choosing the right values of the parameters. This will convert the .ply files in a bunch of .txt (stored in the data folder) files in order to make the next steps easier.
6. Run the MatTotxt() function in the matConverter.py file. This will convert the dataset's .mat 3D models in a .txt format and store them in a groudtruth folder.
7. Run distancesTest() in test.py to find out how much the two models are similar. If you want an overview of the accuracy of the different poses, you can also run posesPrecision().
8. To build a heatmap of the alignment, you can run the open3Dheatmap() function in the heatmap.py file.

Parameters

• plyTotxt(): directory stands for the directory where you can find the .ply files. If you follow the steps above, this should be set to 'filePlyRete/'; compressionLevel stands for the sampling parameter K of the KNNSearch algorithm: since a single model has over 170.000 points, you will probably need to sample the model (depending on your hardware). If you have a single-node cluster, we recommend you to leave this parameter to its default value; radius stands for the maximum distance (from the nose tip) of the useful points: the CNN we've used reconstructs the ears, while the ground truth models have not also those points so, to achieve a better alignment, we've focused just on the face setting this parameter properly.
• MatTotxt(): same as plyTotxt(), but with ground truth Matlab files.
• distancesTest(): the FaceWarehouse dataset is structured this way: there are 150 candidates and 20 poses for each candidate. This means we can choose a subset of the candidates and also a subset of poses to run the test. testers and poses are two lists in which you just need to put the number of the testers and the poses you want to test.
• posesPrecision(): same as distancesTest()
• open3Dheatmap(): target stands for the ground truth .txt model file path, while source stands for the CNN .txt model file path.

References

[1] A. Tran, T. Hassner, I. Masi, E. Paz, Y. Nirkin, G. Medioni, Extreme 3D Face Reconstruction: Seeing Through Occlusions, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2018.

[2] Cao Chen, Yanlin Weng, Shun Zhou, Yiying Tong, Kun Zhou: "FaceWarehouse: a 3D Facial Expression Database for Visual Computing", IEEE Transactions on Visualization and Computer Graphics, 20(3): 413-425, 2014.

[3] Andriy Myronenko and Xubo Song, Point Set Registration: Coherent Point Drift, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 32, issue 12, pp. 2262-2275, 2009.